Importance: Autism spectrum disorder (ASD) is known to be more prevalent among males than females in the general population. Although overall risk of recurrence of ASD among siblings has been estimated to be between 6.1% and 24.7%, information on sex-specific recurrence patterns is lacking.
Objective: To estimate high-confidence sex-specific recurrence rates of ASD among siblings.
Design, Setting, and Participants: This observational study used an administrative database to measure the incidence of ASD among children in 1 583 271 families (37 507 with at least 1 diagnosis of ASD) enrolled in commercial health care insurance plans at a large US managed health care company from January 1, 2008, through February 29, 2016. Families in the study had 2 children who were observed for at least 12 months between 4 and 18 years of age.
Main Outcomes and Measures: The primary measure of ASD recurrence was defined as the diagnosis of ASD in a younger sibling of an older sibling with an ASD diagnosis.
Results: Among the 3 166 542 children (1 547 266 females and 1 619 174 males; mean [SD] age, 11.2 [4.7] years) in the study, the prevalence of ASD was 1.96% (95% CI, 1.94%-1.98%) among males and 0.50% (95% CI, 0.49%-0.51%) among females. When a male was associated with risk in the family, ASD was diagnosed in 4.2% (95% CI, 3.8%-4.7%) of female siblings and 12.9% (95% CI, 12.2%-13.6%) of male siblings. When a female was associated with risk in the family, ASD was diagnosed in 7.6% (95% CI, 6.5%-8.9%) of female siblings and 16.7% (95% CI, 15.2%-18.4%) of male siblings.
Conclusions and Relevance: These findings are in agreement with the higher rates of ASD observed among males than among females in the general population. Our study provides more specific guidance for the screening and counseling of families and may help inform future investigations into the environmental and genetic factors that confer risk of ASD.
INTRODUCTION: Little is known about long term survival risk factors in critically ill burn patients who survive hospitalization. We hypothesized that patients with major burns who survive hospitalization would have favorable long term outcomes.
METHODS: We performed a two center observational cohort study in 365 critically ill adult burn patients who survived to hospital discharge. The exposure of interest was major burn defined a priori as >20% total body surface area burned [TBSA]. The modified Baux score was determined by age + %TBSA+ 17(inhalational injury). The primary outcome was all-cause 5year mortality based on the US Social Security Administration Death Master File. Adjusted associations were estimated through fitting of multivariable logistic regression models. Our final model included adjustment for inhalational injury, presence of 3rd degree burn, gender and the acute organ failure score, a validated ICU risk-prediction score derived from age, ethnicity, surgery vs. medical patient type, comorbidity, sepsis and acute organ failure covariates. Time-to-event analysis was performed using Cox proportional hazard regression.
RESULTS: Of the cohort patients studied, 76% were male, 29% were non white, 14% were over 65, 32% had TBSA >20%, and 45% had inhalational injury. The mean age was 45, 92% had 2nd degree burns, 60% had 3rd degree burns, 21% received vasopressors, and 26% had sepsis. The mean TBSA was 20.1%. The mean modified Baux score was 72.8. Post hospital discharge 5year mortality rate was 9.0%. The 30day hospital readmission rate was 4%. Patients with major burns were significantly younger (41 vs. 47 years) had a significantly higher modified Baux score (89 vs. 62), and had significantly higher comorbidity, acute organ failure, inhalational injury and sepsis (all P<0.05). There were no differences in gender and the acute organ failure score between major and non-major burns. In the multivariable logistic regression model, major burn was associated with a 3 fold decreased odds of 5year post-discharge mortality compared to patients with TBSA<20% [OR=0.29 (95%CI 0.11-0.78; P=0.014)]. The adjusted model showed good discrimination [AUC 0.81 (95%CI 0.74-0.89)] and calibration (Hosmer-Lemeshow χ2 P=0.67). Cox proportional hazard multivariable regression modeling, adjusting for inhalational injury, presence of 3rd degree burn, gender and the acute organ failure score, showed that major burn was predictive of lower mortality following hospital admission [HR=0.34 (95% CI 0.15-0.76; P=0.009)]. The modified Baux score was not predictive for mortality following hospital discharge [OR 5year post-discharge mortality=1.00 (95%CI 0.99-1.02; P=0.74); HR for post-discharge mortality=1.00 (95% CI 0.99-1.02; P=0.55)].
CONCLUSIONS: Critically ill patients with major burns who survive to hospital discharge have decreased 5year mortality compared to those with less severe burns. ICU Burn unit patients who survive to hospital discharge are younger with less comorbidities. The observed relationship is likely due to the relatively higher physiological reserve present in those who survive a Burn ICU course which may provide for a survival advantage during recovery after major burn.
Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10(-37)). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient's note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.
BACKGROUND: Best practice for statistical methodology in cell-based dose-response studies has yet to be established. We examine the ability of MANOVA to detect trait-associated genetic loci in the presence of gene-gene interactions. We present a novel Bayesian nonparametric method designed to detect such interactions.
RESULTS: MANOVA and the Bayesian nonparametric approach show good ability to detect trait-associated genetic variants under various possible genetic models. It is shown through several sets of analyses that this may be due to marginal effects being present, even if the underlying genetic model does not explicitly contain them.
CONCLUSIONS: Understanding how genetic interactions affect drug response continues to be a critical goal. MANOVA and the novel Bayesian framework present a trade-off between computational complexity and model flexibility.
BackgroundDiscovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.ResultsA non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.ConclusionsThe proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.
Cell line cytotoxicity assays have become increasingly popular approaches for genetic and genomic studies of differential cytotoxic response. There are an increasing number of success stories, but relatively little evaluation of the statistical approaches used in such studies. In the vast majority of these studies, concentration response is summarized using curve-fitting approaches, and then summary measure(s) are used as the phenotype in subsequent genetic association studies. The curve is usually summarized by a single parameter such as the curve's inflection point (e.g. the EC/IC50). Such modeling makes major assumptions and has statistical limitations that should be considered. In the current review, we discuss the limitations of the EC/IC50 as a phenotype in association studies, and highlight some potential limitations with a simulation experiment. Finally, we discuss some alternative analysis approaches that have been shown to be more robust.
In recent years, the Hamiltonian Monte Carlo (HMC) algorithm has been found to work more efficiently compared to other popular Markov Chain Monte Carlo (MCMC) methods (such as random walk Metropolis-Hastings) in generating samples from a posterior distribution. A general framework for HMC based on the use of graphical processing units (GPUs) is shown to greatly reduce the computing time needed for Bayesian inference. The most expensive computational tasks in HMC are the evaluation of the posterior kernel and computing its gradient with respect to the parameters of interest. One of primary goals of this article to show that by expressing each of these tasks in terms of simple matrix or element-wise operations and maintaining persistent objects in GPU memory, the computational time can be drastically reduced. By using GPU objects to perform the entire HMC simulation, most of the latency penalties associated with transferring data from main to GPU memory can be avoided. Thus, the proposed computational framework is conceptually very simple, but also is general enough to be applied to most problems that use HMC sampling. For clarity of exposition, the effectiveness of the proposed approach is demonstrated in the high-dimensional setting on a standard statistical model - multinomial regression. Using GPUs, analyses of data sets that were previously intractable for fully Bayesian approaches due to the prohibitively high computational cost are now feasible.
Zebrafish (Danio rerio) is an emerging toxicity screening model for both human health and ecology. As part of the Computational Toxicology Research Program of the U.S. EPA, the toxicity of the 309 ToxCast™ Phase I chemicals was assessed using a zebrafish screen for developmental toxicity. All exposures were by immersion from 6-8 h post fertilization (hpf) to 5 days post fertilization (dpf); nominal concentration range of 1 nM-80 μM. On 6 dpf larvae were assessed for death and overt structural defects. Results revealed that the majority (62%) of chemicals were toxic to the developing zebrafish; both toxicity incidence and potency was correlated with chemical class and hydrophobicity (logP); and inter-and intra-plate replicates showed good agreement. The zebrafish embryo screen, by providing an integrated model of the developing vertebrate, compliments the ToxCast assay portfolio and has the potential to provide information relative to overt and organismal toxicity.
An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC(50)) and efficacy (E(max)) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data.
Primary human hepatocyte cultures are useful in vitro model systems of human liver because when cultured under appropriate conditions the hepatocytes retain liver-like functionality such as metabolism, transport, and cell signaling. This model system was used to characterize the concentration- and time-response of the 320 ToxCast chemicals for changes in expression of genes regulated by nuclear receptors. Fourteen gene targets were monitored in quantitative nuclease protection assays: six representative cytochromes P-450, four hepatic transporters, three Phase II conjugating enzymes, and one endogenous metabolism gene involved in cholesterol synthesis. These gene targets are sentinels of five major signaling pathways: AhR, CAR, PXR, FXR, and PPARalpha. Besides gene expression, the relative potency and efficacy for these chemicals to modulate cellular health and enzymatic activity were assessed. Results demonstrated that the culture system was an effective model of chemical-induced responses by prototypical inducers such as phenobarbital and rifampicin. Gene expression results identified various ToxCast chemicals that were potent or efficacious inducers of one or more of the 14 genes, and by inference the 5 nuclear receptor signaling pathways. Significant relative risk associations with rodent in vivo chronic toxicity effects are reported for the five major receptor pathways. These gene expression data are being incorporated into the larger ToxCast predictive modeling effort.